To improve the prediction accuracy of intersection short-term traffic flow

a new CEEMDAN-PE-OSELM model is developed based on the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)

permutation entropy(PE) and online sequential extreme learning machine(OSELM). Firstly

traffic flow historical time series are decomposed by CEEMDAN algorithm. Secondly

PE algorithm is used to recombine the IMF components obtained by CEEMDAN

and a series of restructured subsequences can be obtained

which have a significant difference in terms of complexity. Then

the OSELM prediction models are proposed for each restructured subsequence respectively

and the final results are got by adding the prediction results. Finally

a typical intersection is verified the effect and performance of the hybrid prediction model. Results show that the values of MAE

MAPE and MSE of CEEMDAN-PE-OSELM prediction model are lower than other models

and get a minimal error. The EC value of the improved model is 0.963

which is higher than that of ARIMA model (0.898) and the most close to 1. The CEEMDAN-PE-OSELM prediction model has the highest precision and best stability

and the errors decrease obviously.